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Bayesian Forecasting using Stochastic Search Variable Selection in a VAR Subject to Breaks

Markus Jochmann (), Gary Koop () and Rodney Strachan ()

Working Paper series from Rimini Centre for Economic Analysis

Abstract: This paper builds a model which has two extensions over a standard VAR. The first of these is stochastic search variable selection, which is an automatic model selection device which allows for coefficients in a possibly over-parameterized VAR to be set to zero. The second allows for an unknown number of structural breaks in the VAR parameters. We investigate the in-sample and forecasting performance of our model in an application involving a commonly-used US macro-economic data set. We find that, in-sample, these extensions clearly are warranted. In a recursive forecasting exercise, we find moderate improvements over a standard VAR, although most of these improvements are due to the use of stochastic search variable selection rather than the inclusion of breaks.

New Economics Papers: this item is included in nep-ets and nep-for
Date: 2008-01
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http://www.rcea.org/RePEc/pdf/wp19_08.pdf

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Journal Article: Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks (2010) Downloads
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